Abstract : Cognitive radio appears as a natural solution to the complexity and scaling issues stemming from the increasing popularity of wireless communications and the radio technologies evolution. A cognitive radio is a smart agent adapting its operation to its context in order to 1 respect the regulation constraints on spectrum access, 2 satisfy the user needs in terms of Quality of Service, and 3 ensure an optimized management of the available resources e.g. network, radio and device hardware resources. This new paradigm is directly linked to the development of embedded intelligence, subject of this PhD thesis.This dissertation details the design of a cognitive engine structuring the reasoning and learning operations required for the supervision of the dynamic reconfiguration process. The proposed solution follows an original approach based on a qualitative modeling of the cognitive design problem.The cognitive engine reasons autonomously by relying on the order relationships defined by two scales dynamically set according to the context. It navigates along these two scales to search for an adapted configuration with a concern on efficiency and optimality. It exploits its predictive capabilities to estimate the impact of the radio environment on the available configurations performance number of transmission errors tolerable. It evaluates the configurations compatible with the service thanks to a grading system assessing the alternatives satisfaction with regard to the optimization objectives reduce the energy consumption, maximize the data rate. If necessary, a design experience is triggered for exploring the design space online. The memorized knowledge is then put to test in order to come closer to the optimal behavior. The experiment is designed dynamically according to the environment feedbacks. The cognitive engine takes also advantage of embedded expert knowledge to limit the experimental risks. It accumulates experience progressively and it learns to appeal less often to experimentation in order to exploit its knowledge that became reliable.We have tested our approach on two case studies of cognitive waveform design. The results obtained confirm the pertinence of the proposed cognitive mechanisms. The cognitive engine manages to find the optimal solution for most of the processed problems 95% in average andit keeps increasing its efficiency in the search of an appropriate configuration.Our modeling method combines efficiently the power of learning systems with knowledge stemming from telecommunications expertise. The cognitive engine is designed with a great decision autonomy through its ability to reason, explore online and learn incrementally. Moreover, we have proposed advanced mechanisms to optimize its behavior in order to make it an effective cognitive solution for many design problems.